Overview

Dataset statistics

Number of variables11
Number of observations79215
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 MiB
Average record size in memory88.0 B

Variable types

Numeric9
Categorical2

Alerts

X_01 is highly correlated with X_06High correlation
X_06 is highly correlated with X_01 and 1 other fieldsHigh correlation
X_03 is highly correlated with X_06High correlation
X_10 is highly correlated with X_11High correlation
X_11 is highly correlated with X_10High correlation
X_01 is highly correlated with X_05 and 1 other fieldsHigh correlation
X_05 is highly correlated with X_01High correlation
X_06 is highly correlated with X_01High correlation
X_10 is highly correlated with X_11High correlation
X_11 is highly correlated with X_10High correlation
X_10 is highly correlated with X_11High correlation
X_11 is highly correlated with X_10High correlation
df_index is highly correlated with X_03High correlation
X_01 is highly correlated with X_05 and 1 other fieldsHigh correlation
X_05 is highly correlated with X_01High correlation
X_06 is highly correlated with X_01 and 1 other fieldsHigh correlation
X_03 is highly correlated with df_index and 1 other fieldsHigh correlation
X_10 is highly correlated with X_11High correlation
X_11 is highly correlated with X_10High correlation
X_08 is highly correlated with X_09High correlation
X_09 is highly correlated with X_08High correlation
X_10 is highly skewed (γ1 = 35.00400665) Skewed
df_index is uniformly distributed Uniform
X_10 has 79150 (99.9%) zeros Zeros

Reproduction

Analysis started2022-08-08 03:58:17.725028
Analysis finished2022-08-08 03:58:35.283472
Duration17.56 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM

Distinct39608
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19803.25
Minimum0
Maximum39607
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-08T12:58:35.513887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1980
Q19901.5
median19803
Q329705
95-th percentile37626.3
Maximum39607
Range39607
Interquartile range (IQR)19803.5

Descriptive statistics

Standard deviation11433.77256
Coefficient of variation (CV)0.5773684907
Kurtosis-1.199999999
Mean19803.25
Median Absolute Deviation (MAD)9902
Skewness1.6561712 × 10-9
Sum1568714449
Variance130731155.1
MonotonicityNot monotonic
2022-08-08T12:58:35.668473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02
 
< 0.1%
264072
 
< 0.1%
264002
 
< 0.1%
264012
 
< 0.1%
264022
 
< 0.1%
264032
 
< 0.1%
264042
 
< 0.1%
264052
 
< 0.1%
264062
 
< 0.1%
264082
 
< 0.1%
Other values (39598)79195
> 99.9%
ValueCountFrequency (%)
02
< 0.1%
12
< 0.1%
22
< 0.1%
32
< 0.1%
42
< 0.1%
52
< 0.1%
62
< 0.1%
72
< 0.1%
82
< 0.1%
92
< 0.1%
ValueCountFrequency (%)
396071
< 0.1%
396062
< 0.1%
396052
< 0.1%
396042
< 0.1%
396032
< 0.1%
396022
< 0.1%
396012
< 0.1%
396002
< 0.1%
395992
< 0.1%
395982
< 0.1%

X_01
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.40402126
Minimum53.209
Maximum86.859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-08T12:58:35.819893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum53.209
5-th percentile64.425
Q166.465
median68.504
Q369.524
95-th percentile73.603
Maximum86.859
Range33.65
Interquartile range (IQR)3.059

Descriptive statistics

Standard deviation2.659533657
Coefficient of variation (CV)0.03887978525
Kurtosis0.7509481017
Mean68.40402126
Median Absolute Deviation (MAD)2.039
Skewness0.4545739545
Sum5418624.544
Variance7.073119272
MonotonicityNot monotonic
2022-08-08T12:58:35.938182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
68.50413039
16.5%
66.46512495
15.8%
69.52411766
14.9%
67.48511220
14.2%
70.5445784
7.3%
71.5635779
7.3%
65.4455493
6.9%
64.4254659
 
5.9%
72.5832420
 
3.1%
73.6032238
 
2.8%
Other values (23)4322
 
5.5%
ValueCountFrequency (%)
53.2091
 
< 0.1%
55.2482
 
< 0.1%
56.2681
 
< 0.1%
57.2872
 
< 0.1%
58.3079
 
< 0.1%
59.32719
 
< 0.1%
60.34733
 
< 0.1%
61.366173
 
0.2%
62.386467
 
0.6%
63.4061543
1.9%
ValueCountFrequency (%)
86.8591
 
< 0.1%
85.842
 
< 0.1%
84.823
 
< 0.1%
83.84
 
< 0.1%
82.786
 
< 0.1%
81.7615
 
< 0.1%
80.74111
 
< 0.1%
79.72129
 
< 0.1%
78.70266
0.1%
77.68299
0.1%

X_02
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size619.0 KiB
103.32
66143 
103.321
13072 

Length

Max length7
Median length6
Mean length6.165019251
Min length6

Characters and Unicode

Total characters488362
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row103.32
2nd row103.321
3rd row103.32
4th row103.32
5th row103.32

Common Values

ValueCountFrequency (%)
103.3266143
83.5%
103.32113072
 
16.5%

Length

2022-08-08T12:58:36.063908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-08T12:58:36.231694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
103.3266143
83.5%
103.32113072
 
16.5%

Most occurring characters

ValueCountFrequency (%)
3158430
32.4%
192287
18.9%
079215
16.2%
.79215
16.2%
279215
16.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number409147
83.8%
Other Punctuation79215
 
16.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3158430
38.7%
192287
22.6%
079215
19.4%
279215
19.4%
Other Punctuation
ValueCountFrequency (%)
.79215
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common488362
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3158430
32.4%
192287
18.9%
079215
16.2%
.79215
16.2%
279215
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII488362
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3158430
32.4%
192287
18.9%
079215
16.2%
.79215
16.2%
279215
16.2%

X_05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct462
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.3372487
Minimum101.734
Maximum103.161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-08T12:58:36.349032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum101.734
5-th percentile101.889
Q1101.949
median102.007
Q3103.144
95-th percentile103.157
Maximum103.161
Range1.427
Interquartile range (IQR)1.195

Descriptive statistics

Standard deviation0.5481525175
Coefficient of variation (CV)0.005356334323
Kurtosis-1.337063559
Mean102.3372487
Median Absolute Deviation (MAD)0.077
Skewness0.7933924669
Sum8106645.152
Variance0.3004711824
MonotonicityNot monotonic
2022-08-08T12:58:36.491160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103.1572498
 
3.2%
103.1582022
 
2.6%
103.1561789
 
2.3%
103.1551634
 
2.1%
103.1541618
 
2.0%
103.1531586
 
2.0%
103.1521373
 
1.7%
103.1511195
 
1.5%
103.151088
 
1.4%
103.149898
 
1.1%
Other values (452)63514
80.2%
ValueCountFrequency (%)
101.7341
 
< 0.1%
101.7541
 
< 0.1%
101.7741
 
< 0.1%
101.7781
 
< 0.1%
101.7822
< 0.1%
101.7861
 
< 0.1%
101.7871
 
< 0.1%
101.7883
< 0.1%
101.7892
< 0.1%
101.794
< 0.1%
ValueCountFrequency (%)
103.1615
 
< 0.1%
103.16486
 
0.6%
103.159862
 
1.1%
103.1582022
2.6%
103.1572498
3.2%
103.1561789
2.3%
103.1551634
2.1%
103.1541618
2.0%
103.1531586
2.0%
103.1521373
1.7%

X_06
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.59093362
Minimum61.726
Maximum87.219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-08T12:58:36.626167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum61.726
5-th percentile66.825
Q168.864
median69.884
Q371.923
95-th percentile73.963
Maximum87.219
Range25.493
Interquartile range (IQR)3.059

Descriptive statistics

Standard deviation2.255384568
Coefficient of variation (CV)0.03195006005
Kurtosis0.6872052854
Mean70.59093362
Median Absolute Deviation (MAD)1.02
Skewness0.4405082341
Sum5591860.807
Variance5.086759552
MonotonicityNot monotonic
2022-08-08T12:58:36.746403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
69.88417043
21.5%
71.92312720
16.1%
68.86412426
15.7%
70.90411011
13.9%
67.8456443
 
8.1%
72.9435614
 
7.1%
73.9635128
 
6.5%
66.8253766
 
4.8%
74.9832405
 
3.0%
65.805761
 
1.0%
Other values (16)1898
 
2.4%
ValueCountFrequency (%)
61.7265
 
< 0.1%
62.7468
 
< 0.1%
63.76642
 
0.1%
64.785310
 
0.4%
65.805761
 
1.0%
66.8253766
 
4.8%
67.8456443
 
8.1%
68.86412426
15.7%
69.88417043
21.5%
70.90411011
13.9%
ValueCountFrequency (%)
87.2192
 
< 0.1%
86.22
 
< 0.1%
85.183
 
< 0.1%
84.165
 
< 0.1%
83.146
 
< 0.1%
82.1215
 
< 0.1%
81.10113
 
< 0.1%
80.08134
 
< 0.1%
79.06293
0.1%
78.042146
0.2%

X_03
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct298
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.83213722
Minimum55.57
Maximum89.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-08T12:58:36.885171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum55.57
5-th percentile62.77
Q165.07
median67.27
Q371.77
95-th percentile80.27
Maximum89.17
Range33.6
Interquartile range (IQR)6.7

Descriptive statistics

Standard deviation5.178511143
Coefficient of variation (CV)0.07523391474
Kurtosis0.2512423422
Mean68.83213722
Median Absolute Deviation (MAD)2.8
Skewness0.9801863773
Sum5452537.75
Variance26.81697766
MonotonicityNot monotonic
2022-08-08T12:58:37.031765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65.171019
 
1.3%
65.771007
 
1.3%
65.87990
 
1.2%
66.07988
 
1.2%
65.07970
 
1.2%
66.37968
 
1.2%
65.57956
 
1.2%
65.97951
 
1.2%
65.67951
 
1.2%
65.37950
 
1.2%
Other values (288)69465
87.7%
ValueCountFrequency (%)
55.571
 
< 0.1%
56.471
 
< 0.1%
56.772
< 0.1%
56.871
 
< 0.1%
56.971
 
< 0.1%
57.071
 
< 0.1%
57.271
 
< 0.1%
57.471
 
< 0.1%
57.671
 
< 0.1%
57.773
< 0.1%
ValueCountFrequency (%)
89.171
 
< 0.1%
88.671
 
< 0.1%
87.771
 
< 0.1%
87.671
 
< 0.1%
87.571
 
< 0.1%
87.171
 
< 0.1%
86.873
< 0.1%
86.771
 
< 0.1%
86.672
< 0.1%
86.571
 
< 0.1%

X_10
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002504576154
Minimum0
Maximum3.6
Zeros79150
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-08T12:58:37.162412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3.6
Range3.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0875087213
Coefficient of variation (CV)34.93953305
Kurtosis1226.741262
Mean0.002504576154
Median Absolute Deviation (MAD)0
Skewness35.00400665
Sum198.4
Variance0.007657776303
MonotonicityNot monotonic
2022-08-08T12:58:37.263950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
079150
99.9%
320
 
< 0.1%
2.917
 
< 0.1%
3.114
 
< 0.1%
3.36
 
< 0.1%
3.25
 
< 0.1%
3.61
 
< 0.1%
3.51
 
< 0.1%
2.81
 
< 0.1%
ValueCountFrequency (%)
079150
99.9%
2.81
 
< 0.1%
2.917
 
< 0.1%
320
 
< 0.1%
3.114
 
< 0.1%
3.25
 
< 0.1%
3.36
 
< 0.1%
3.51
 
< 0.1%
3.61
 
< 0.1%
ValueCountFrequency (%)
3.61
 
< 0.1%
3.51
 
< 0.1%
3.36
 
< 0.1%
3.25
 
< 0.1%
3.114
 
< 0.1%
320
 
< 0.1%
2.917
 
< 0.1%
2.81
 
< 0.1%
079150
99.9%

X_11
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size619.0 KiB
0.0
79159 
0.5
 
25
0.6
 
24
0.4
 
6
0.7
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters237645
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.079159
99.9%
0.525
 
< 0.1%
0.624
 
< 0.1%
0.46
 
< 0.1%
0.71
 
< 0.1%

Length

2022-08-08T12:58:37.386626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-08T12:58:37.509033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.079159
99.9%
0.525
 
< 0.1%
0.624
 
< 0.1%
0.46
 
< 0.1%
0.71
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0158374
66.6%
.79215
33.3%
525
 
< 0.1%
624
 
< 0.1%
46
 
< 0.1%
71
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number158430
66.7%
Other Punctuation79215
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0158374
> 99.9%
525
 
< 0.1%
624
 
< 0.1%
46
 
< 0.1%
71
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.79215
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common237645
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0158374
66.6%
.79215
33.3%
525
 
< 0.1%
624
 
< 0.1%
46
 
< 0.1%
71
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII237645
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0158374
66.6%
.79215
33.3%
525
 
< 0.1%
624
 
< 0.1%
46
 
< 0.1%
71
 
< 0.1%

X_07
Real number (ℝ≥0)

Distinct1684
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.43642643
Minimum13.39
Maximum163.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-08T12:58:37.632701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum13.39
5-th percentile26.17
Q127.89
median28.84
Q329.87
95-th percentile32.7
Maximum163.86
Range150.47
Interquartile range (IQR)1.98

Descriptive statistics

Standard deviation7.608329676
Coefficient of variation (CV)0.2584664851
Kurtosis282.2671693
Mean29.43642643
Median Absolute Deviation (MAD)0.98
Skewness16.24178307
Sum2331806.52
Variance57.88668045
MonotonicityNot monotonic
2022-08-08T12:58:37.776802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.82278
 
0.4%
28.86272
 
0.3%
28.63270
 
0.3%
28.74268
 
0.3%
29.06261
 
0.3%
28.92255
 
0.3%
28.78255
 
0.3%
28.71254
 
0.3%
28.53253
 
0.3%
28.49253
 
0.3%
Other values (1674)76596
96.7%
ValueCountFrequency (%)
13.391
< 0.1%
14.141
< 0.1%
15.011
< 0.1%
22.481
< 0.1%
23.251
< 0.1%
23.461
< 0.1%
23.921
< 0.1%
23.952
< 0.1%
23.961
< 0.1%
23.971
< 0.1%
ValueCountFrequency (%)
163.86203
0.3%
163.851
 
< 0.1%
163.812
 
< 0.1%
163.791
 
< 0.1%
163.782
 
< 0.1%
163.771
 
< 0.1%
163.732
 
< 0.1%
163.691
 
< 0.1%
163.652
 
< 0.1%
163.641
 
< 0.1%

X_08
Real number (ℝ≥0)

HIGH CORRELATION

Distinct19711
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean163.9109997
Minimum28.59
Maximum2387.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-08T12:58:37.933187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum28.59
5-th percentile74.07
Q1105.87
median115.04
Q3132.11
95-th percentile328.026
Maximum2387.44
Range2358.85
Interquartile range (IQR)26.24

Descriptive statistics

Standard deviation219.8447029
Coefficient of variation (CV)1.341244354
Kurtosis51.1574496
Mean163.9109997
Median Absolute Deviation (MAD)10.79
Skewness6.697248083
Sum12984209.84
Variance48331.6934
MonotonicityNot monotonic
2022-08-08T12:58:38.080569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
115.6649
 
0.1%
116.1147
 
0.1%
113.0246
 
0.1%
114.1745
 
0.1%
111.7745
 
0.1%
114.3445
 
0.1%
115.9644
 
0.1%
113.9744
 
0.1%
115.2942
 
0.1%
114.5242
 
0.1%
Other values (19701)78766
99.4%
ValueCountFrequency (%)
28.591
< 0.1%
31.881
< 0.1%
38.461
< 0.1%
42.41
< 0.1%
42.411
< 0.1%
42.441
< 0.1%
42.531
< 0.1%
42.641
< 0.1%
42.741
< 0.1%
42.851
< 0.1%
ValueCountFrequency (%)
2387.4415
< 0.1%
2387.434
 
< 0.1%
2387.427
< 0.1%
2387.411
 
< 0.1%
2387.383
 
< 0.1%
2387.362
 
< 0.1%
2387.331
 
< 0.1%
2387.32
 
< 0.1%
2387.261
 
< 0.1%
2387.241
 
< 0.1%

X_09
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13261
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225.2985645
Minimum37.58
Maximum637.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-08T12:58:38.227805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum37.58
5-th percentile109.9
Q1188.48
median234.69
Q3263.98
95-th percentile300.44
Maximum637.54
Range599.96
Interquartile range (IQR)75.5

Descriptive statistics

Standard deviation66.50051019
Coefficient of variation (CV)0.2951661513
Kurtosis5.453370941
Mean225.2985645
Median Absolute Deviation (MAD)36.8
Skewness0.49983987
Sum17847025.79
Variance4422.317855
MonotonicityNot monotonic
2022-08-08T12:58:38.363845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.581728
 
2.2%
250.3830
 
< 0.1%
255.7128
 
< 0.1%
254.6327
 
< 0.1%
253.3426
 
< 0.1%
263.4126
 
< 0.1%
260.3924
 
< 0.1%
251.7624
 
< 0.1%
259.1624
 
< 0.1%
255.4724
 
< 0.1%
Other values (13251)77254
97.5%
ValueCountFrequency (%)
37.581728
2.2%
87.611
 
< 0.1%
87.682
 
< 0.1%
87.761
 
< 0.1%
87.812
 
< 0.1%
87.891
 
< 0.1%
87.911
 
< 0.1%
87.931
 
< 0.1%
87.941
 
< 0.1%
87.961
 
< 0.1%
ValueCountFrequency (%)
637.541
< 0.1%
637.491
< 0.1%
637.451
< 0.1%
637.281
< 0.1%
636.571
< 0.1%
635.751
< 0.1%
635.61
< 0.1%
635.272
< 0.1%
635.121
< 0.1%
634.911
< 0.1%

Interactions

2022-08-08T12:58:33.522730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:22.808419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:24.196229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:25.524229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:27.026330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:28.300048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:29.591139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:31.008193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:32.283492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:33.670900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:22.982725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:24.355803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:25.684271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:27.178896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:28.451642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:29.739771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:31.157762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:32.428447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:33.807564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:23.135315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:24.503407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:25.963792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:27.319740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:28.592772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:29.881135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:31.298416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:32.564620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:33.951154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:23.291926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:24.659888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:26.124797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:27.463423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:28.742474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:30.029800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:31.447023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:32.709784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:34.081360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:23.442652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:24.797495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:26.268745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:27.594763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:28.875795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:30.162478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:31.579665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:32.839565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:34.221535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:23.594275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:24.946124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:26.422940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:27.740374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:29.020715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:30.306123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:31.722795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:32.978192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:34.362315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:23.746145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:25.101090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:26.578599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:27.884987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:29.169316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:30.451185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:31.866979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:33.117789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:34.504164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:23.902700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:25.245735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:26.733867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:28.028690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:29.314952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:30.735716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:32.010628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:33.256443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:34.636173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:24.048421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:25.385395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:26.877728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:28.162380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:29.454589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:30.871913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:32.147291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-08T12:58:33.389097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-08T12:58:38.622860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-08T12:58:38.837160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-08T12:58:39.010699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-08T12:58:39.170742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-08T12:58:39.289418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-08T12:58:34.820675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-08T12:58:35.083984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexX_01X_02X_05X_06X_03X_10X_11X_07X_08X_09
0070.544103.320101.89274.98367.470.00.029.4562.38245.71
1169.524103.321101.94472.94365.170.00.028.7361.23233.61
2272.583103.320103.15372.94364.070.00.028.81105.77272.20
3371.563103.320101.97177.02267.570.00.028.92115.21255.36
4469.524103.320101.98170.90463.570.00.029.68103.38241.46
5569.524103.320101.89969.88462.770.00.027.9064.97241.85
6671.563103.320101.92173.96366.070.00.029.3069.22237.51
7769.524103.320101.96873.96365.470.00.029.7771.41238.27
8871.563103.320101.99672.94366.270.00.029.7668.75232.23
9971.563103.320101.99077.02268.970.00.028.9766.88228.22

Last rows

df_indexX_01X_02X_05X_06X_03X_10X_11X_07X_08X_09
792053959868.504103.321102.05368.86464.670.00.031.75115.40191.02
792063959967.485103.320102.03870.90467.070.00.030.74119.53197.42
792073960069.524103.320103.14369.88463.870.00.029.80114.08198.80
792083960168.504103.320103.13568.86462.770.00.031.99117.76292.87
792093960267.485103.320102.05069.88464.970.00.029.59114.22291.71
792103960368.504103.320103.15768.86463.970.00.029.49116.35284.16
792113960468.504103.320103.13768.86461.370.00.032.29116.28272.41
792123960569.524103.320103.14969.88463.670.00.030.00113.05295.54
792133960667.485103.321103.14867.84561.770.00.032.05115.05267.26
792143960771.563103.320103.15871.92363.070.00.031.14102.22215.85